Beyond the numbers: The role of people and processes in central bank forecasting

We complement the previous discussions of Bernanke’s review of the Bank of England’s forecasting activities and highlight directions for future research that are relevant to central banks and the wider forecasting community. Decisions in central banks, such as monetary policy ones, are hardly algorithmic and are often influenced by policy and current soft contextual information, introducing challenges into evaluating and specifying forecasts. The use of alternatives to standard econometric models is highlighted in the Bernanke report and other commentaries in this series. These methodological alternatives require both more research, to be validly applied and evaluated, and a cultural shift for those with forecasting responsibilities in central banks. Critically, uncertainty estimates in central bank forecasts are hardly purely model-based. How this is done and how to best communicate it to stakeholders and counterparties are fertile areas for research with potentially important implications for market participants. Finally, while academic research often focuses on large, well-funded central banks, there is a significant opportunity to help smaller, less-resourced institutions.